This article presents three methods to forecast accurately the amount of traffic in TCP=IP based networks: a novel neural network ensemble approach and two important adapted time series methods (ARIMA and Holt-Winters). In order to assess their accuracy, several experiments were held using real-world data from two large Internet service providers. In addition, different time scales (5 min, 1 h and 1 day) and distinct forecasting lookaheads were analysed. The experiments with the neural ensemble achieved the best results for 5 min and hourly data, while the Holt-Winters is the best option for the daily forecasts. This research opens possibilities for the development of more efficient traffic engineering and anomaly detection tools, which will result in financial gains from better network resource management.

The forecast of Internet traffic is an important issue that has received few attention from the computer networks field. By improving this task, efficient traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. This paper presents a Neural Network Ensemble (NNE) for the prediction of TCP/IP traffic using a Time Series Forecasting (TSF) point of view. Several experiments were devised by considering real-world data from two large Internet Service Providers. In addition, different time scales (e.g. every five minutes and hourly) and forecasting horizons were analyzed. Overall, the NNE approach is competitive when compared with other TSF methods (e.g. Holt-Winters and ARIMA).Paulo Cortez is with the

Abstract-The forecast of Internet traffic is an important issue that has received few attention from the computer networks field. By improving this task, efficient traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. This paper presents a Neural Network Ensemble (NNE) for the prediction of TCP/IP traffic using a Time Series Forecasting (TSF) point of view. Several experiments were devised by considering real-world data from two large Internet Service Providers. In addition, different time scales (e.g. every five minutes and hourly) and forecasting horizons were analyzed. Overall, the NNE approach is competitive when compared with other TSF methods (e.g. Holt-Winters and ARIMA).

This paper presents a novel spam filtering technique called Symbiotic Filtering (SF) that aggregates distinct local filters from several users to improve the overall performance of spam detection. SF is an hybrid approach combining some features from both Collaborative (CF) and Content-Based Filtering (CBF). It allows for the use of social networks to personalize and tailor the set of filters that serve as input to the filtering. A comparison is performed against the commonly used Naive Bayes CBF algorithm. Several experiments were held with the well-known Enron data, under both fixed and incremental symbiotic groups. We show that our system is competitive in performance and is robust against both dictionary and focused contamination attacks. Moreover, it can be implemented and deployed with few effort and low communication costs, while assuring privacy.

In this work, a novel optimization framework is proposed that allows the improvement of Quality of Service levels in TCP/IP based networks, by configuring the routing weights of link-state protocols such as OSPF. Since this is a NP-hard problem, some algorithms from Evolutionary Computation were considered, working over a mathematical model that allows the definition of flexible cost functions that can take into account several measures of the network behaviour, such as network congestion and end-to-end delays. A number of experiments were performed, over a large set of network topologies, where Evolutionary Algorithms (EAs), Differential Evolution, local search methods and common heuristics were compared. EAs make the most promising alternative leading to solutions with an effective network performance, even under unfavourable scenarios. A number of state of the art multiobjective optimization algorithms were also tested, but the proposed EAs still hold as the most consistent method for network optimization.